Self-training and co-training applied to Spanish named entity recognition

Abstract

The paper discusses the usage of unlabeled data for Spanish Named Entity Recognition. Two techniques have been used: self-training for detecting the entities in the text and co-training for classifying these already detected entities. We introduce a new co-training algorithm, which applies voting techniques in order to decide which unlabeled example should be added into the training set at each iteration. A proposal for improving the performance of the detected entities has been made. A brief comparative study with already existing co-training algorithms is demonstrated. © Springer-Verlag Berlin Heidelberg 2005.